Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 68; H. 12; S. 3564 - 3573
Hauptverfasser:	Qu, Gang, Xiao, Li, Hu, Wenxing, Wang, Junqi, Zhang, Kun, Calhoun, Vince, Wang, Yu-Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Achievement tests Artificial neural networks Biological system modeling Biomarkers Brain Brain mapping Brain modeling Cognition Cognition & reasoning Cognitive ability Data integration Deep learning Embedding fMRI functional connectivity Functional magnetic resonance imaging graph convolutional networks interpretable deep learning Magnetic resonance imaging Manifolds Modal data multi-modal deep learning Neural networks Neuroimaging Performance prediction Predictions Predictive models Regularization Time series analysis
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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